There are 2 repositories under lfw-dataset topic.
A PyTorch implementation of the 'FaceNet' paper for training a facial recognition model with Triplet Loss using the glint360k dataset. A pre-trained model using Triplet Loss is available for download.
A PyTorch Implementation of ShuffleFaceNet.
This repo contains auto encoders and decoders using keras and tensor flow. It shows the exact encoding and decoding with the code part.
Face Recognition with SVM classifier using PCA, ICA, NMF, LDA reduced face vectors
PcaNet, PCA Network, Deep Learning, Face Classification, LFW dataset, SVM
Face Recognition Implementation using PCA, eigenfaces, and SVM
PyTorch implementation of LS-CNN: Characterizing Local Patches at Multiple Scales for Face Recognition
Final Project Of Computational Intelligence - Fall 2021 - LightGBM, RandomForest and StackingClassifier
Image Inpainting using Context Encoders
Autoencoders test from Coursera's Advanced Machine Learning - Intro to Deep Learning course.
Face Recognition with convolutional neural network (CNN) on Labeled Faces in the Wild (LFW) dataset
Analysing different dimensionality reduction techniques and svm
Facial Images Generation With Generative Adversarial Network (GAN)
A simple colab notebook on validation of face recognition model on LFW test dataset.
Face Recognition in Caffe using different VGGNet architectures on ColorFeret and LFW datasets
Fast semi-supervised face recognition model using graph theory and fast computer vision methods.
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GAN trained on the preprocessed LFW dataset with an analysis of peculiar generation artifacts.
ML model for grouping similar faces using cutting-edge deep learning and computer vision techniques. Custom dataset of 300 images captures comprehensive facial variations. Siamese network outperforms Face-Net, delivering reliable clustering results.
Implementation and training of facial identification model on LFW dataset.
Code for training and parameter tuning of a machine learning model for non-linear aggregation of image denoising estimators using COBRA combined regression strategy. The face images used for training and testing are taken from the Labelled Faces in the Wild (LFW) dataset.
Simple application of VGG16 for the recognition of images, obtained from LFW, of a limited number of famous(15) with good performance (greater than 80%)
A recognition process of images contained in the LFW database http://vis-www.cs.umass.edu/lfw/#download is carried out using two models, one based on the minimum distance between training image records and test and another that is an adaptation of the CNN KERAS model https://keras.io/examples/vision/mnist_convnet/. Both models are complementary. A module is also incorporated that takes advantage of the facility of sklearn to implement the SVM model with great simplicity.
Just some implementations of my understanding of Generative models
dog breed classifier based on convnets, using catdog and lfw datasets.
A coolection of tools for organizing directories, specifically converting the Labeled Faces of the Wild (cropped) to a common standard.